液压系统故障诊断断断开方法基于2D时间序列建模和自我注意的融合模型
Haicheng Wang1, Juan Zhou2, Hu Chen3
1College of Energy Environment and Safety Engineering and College of Carbon Metrology, China Jiliang University, Hangzhou, 310018, China.
Scientific reports
|July 7, 2024
概括
本研究介绍了一种使用二维时间建模和注意力机制的先进液压故障诊断模型. 这种新的方法有效地解了液压系统中的复合故障,即使在噪音条件下,诊断准确率也超过97%.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 液压系统在机械和能源方面至关重要.
- 由于系统的复杂性和故障隐藏,智能故障诊断具有挑战性.
研究的目的:
- 开发一种用于液压故障诊断的新方法.
- 为了分离复合故障,并从多个样本率传感器数据中提取特征.
主要方法:
- 变频数据采样用于预处理.
- 2D卷积用于捕捉瞬间和长期特征.
- 对特征融合和脱的概率主义稀疏自我注意力.
主要成果:
- 拟议的模型实现了超过97%的诊断准确度.
- 对不同组件的有效故障解和分类.
- 在噪音条件下强大的性能.
结论:
- 新型号准确诊断液压系统故障.
- 它有效地处理多样采样率传感器数据和复合故障.
- 在液压系统健康监测方面取得了重大进展.
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